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Federated Learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks 23

Federated Learning With Dynamic Staleness Correction for Pri...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Jiajia Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665494571

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. The proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data. © 2021 IEEE.

关键词： Privacy data privacy Costs Smart cities Training data Telecommunication traffic Collaborative work

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IoT-Enabled Smart Agriculture System Using Cognitive computing

IoT-Enabled Smart Agriculture System Using Cognitive Computi...

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advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： Durgam Rajababu Eswararao Boddepalli Rajesh Bhaskar Survase Arunabha Pal Sreenivasulu Dandagala M. Kalyan Chakravarthi Department of EEE School of Engineering SR University Warangal Telangana India Bradley Department of ECE Virginia Tech Blacksburg United States of America Department of Geography E. S. Divekar College Varvand Savitribai Phule Pune University Pune Maharashtra India Department of Soil Science M.S. Swaminathan School of Agriculture Centurion University of Technology and Management Paralakhemindi Odisha India Department of Civil Engineering Annamacharya Institute of Technology and Sciences Rajampet Annamacharya University Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an Internet of Things-enabled smart husbandry system that leverages cognitive computing techniques is the aim of this study. The system includes detectors to gather data on environmental factors like crop health, soil temperature, and humidity in real-time. After that, a centralized platform receives these data. This data is processed by algorithms used in cognitive computing to give growers useful insights like optimal irrigation timing and early detection of crop issues. The application of this method raises agricultural operations’ productivity and enhances decision-making processes’ effectiveness. This study covers the following topics: the system’s armature; the choice and incorporation of Internet of Things bias; the use of cognitive computing models; and the assessment of system performance in actual agricultural scripts. The results show how cognitive computing and the Internet of Things (IoT) may revolutionize traditional husbandry methods into data-driven, intelligent husbandry systems.

关键词： Productivity Computational modeling Decision making Crops Detectors Soil Real-time systems

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An Integrated Framework for Enhanced Learning Environments: IoT-driven Smart Classrooms with Multi-Layered Security Protocols and Adaptive Infrastructure

An Integrated Framework for Enhanced Learning Environments: ...

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advances in computing,Communication, Electrical, and Smart systems (iCACCESS), international conference on

作者： Khandokar Shadman Shayer Mahamudul Hasan Medul Md. Badoruzzaman Jahidul Islam Shuvo Mohammad Rabbu F. M. Mahmudul Haque Department of Electrical & Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Science & Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

The learning environment has experienced an evolution in this era of advanced instructional technologies. Smart classrooms are currently equipped with interactive devices and resources to enhance the effectiveness of learning, and this trend has evolved into an innovative classroom management technique. Conventional classes require a substantial workforce and an administrative team. In a conventional classroom setting, the level of security is generally either limited or completely absent. Considering the circumstances mentioned above, research has been conducted as a provisional resolution. Utilizing the Internet of Things, this research aims to develop a smart classroom that effectively handles real-time information and implements techniques for intelligent security and energy saving. The research utilized ESP32-CAM, a fingerprint sensor, a flame sensor, SIM8001, and an Arduino N ano. The prototype transmits live data to the website server. The hardware implementation results have been thoroughly assessed to verify the project's effective completion.

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Unveiling Mortality Patterns in Ecuador: A K-Modes Clustering Approach to Categorical data Analysis 1

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8th international conference on Information and Communication Technology for intelligent systems, ICTIS 2024

作者： Jimenez-Torres, Adriana Roa, Henry N. Facultad de Ingeniería Pontificia Universidad Católica del Ecuador Av. 12 de Octubre 1076 y Vicente Ramón Roca P.O. Box 17-01-2184 Quito Ecuador

ISBN: (数字)9789819757992

ISBN: (纸本)9789819757985

This study applies the k-modes clustering algorithm to analyze mortality in Ecuador, utilizing categorical data to reveal patterns related to demographic factors and causes of death. We identify distinct mortality profiles by carefully preprocessing a comprehensive dataset to focus on critical variables such as age and province of death. Our analysis uncovers significant disparities in mortality rates driven by socioeconomic and regional influences. These findings highlight the demographic and geographical factors affecting mortality and provide essential insights for public health officials and policymakers to develop targeted interventions. This research significantly advances epidemiological studies, offering a novel perspective on Ecuador’s mortality trends and guiding healthcare policy and resource distribution. We discuss the study’s limitations and future research directions, emphasizing the importance of ongoing analysis to improve public health strategies. This effort seeks to bolster the efficacy of health interventions and policies, striving for a meaningful improvement in public health in Ecuador. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE international conference on Control Science and systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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Bio Algorithms for Resource Optimisation and Analysis of data

Bio Algorithms for Resource Optimisation and Analysis of Dat...

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advances in Electronics, Communication, computing and intelligent Information systems (ICAECIS), international conference on

作者： Madhav Natarajan H Kristipati Pravallika K Chaitanya Dasara Sri Saarthak Ananda M Dept. of Electronics and Communication Engineering PES University Bengaluru India

Wireless sensor nodes are an important part of many modern systems, such as environmental monitoring networks, smart homes, and industrial automation. These nodes are typically small, low-power devices that are deployed in large numbers to collect data and perform various tasks. However, the limited energy resources of these nodes can be a major limitation to their performance and lifetime. Optimal sensor placement not only minimizes the consumption of limited energy of battery-driven sensors but also reduces the sensing of redundant information. Hence it is of utmost importance to optimize the energy consumed by a wireless sensor network for better sustainability. In this project, we focus on bio-inspired optimization algorithms concerning the optimal sensor node placement in WSNs for a smart agriculture setup namely the Particle Swarm Optimization Algorithm and LEACH Algorithm and prove that the latter is the better algorithm suited for energy conservation by soil moisture wireless sensor nodes placed in a WSN and the same can be extended to any real-life application. A comparative analysis is performed between the stochastic heuristic algorithm in PSO and the hierarchical protocol of the LEACH algorithm for energy consumed by the wireless sensor nodes.

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Trends and Challenges in AI-driven Microservices for Cloud-Based Airline Reservation systems: A Review

Trends and Challenges in AI-Driven Microservices for Cloud-B...

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Biman Barua Imon Barua M. Shamim Kaiser Mohammad Jashim Uddin Mozumder Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Department of CSE BGMEA University of Fashion & Technology Turag Dhaka Bangladesh SY Systems Co. Ltd. Nagoya Japan Bangabandhu Sheikh Mujib Medical University Dhaka Bangladesh

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rapid advances in Artificial Intelligence (AI), microservices architecture, and cloud computing highly impact airlines in optimizing reservation systems. This review paper attempts to analyze the ongoing trend, challenges, and future opportunities of AI-enabled microservices toward cloud-based airline reservation systems. AI with machine learning and deep learning supports dynamic pricing, personalized services, and better demand forecasting, transforming booking, pricing, and customer experience. While microservices architecture offers scalability and flexibility, in conjunction with cloud computing, it optimizes airline demands and system performance. At the same time, challenges such as data privacy and security, scalability, interoperability, and costs are still present with adopting cloud-based microservices. Case studies are provided in this paper, as well as a thorough exposition of future directions of AI in airline reservation systems, such as the application of emerging AI techniques like reinforcement learning, evolution toward micro service architecture, and the influence of new cloud computing models such as edge and hybrid clouds. In addition, the work points out research gaps in areas such as real-time predictive analytics and security measures. Findings indicate a transforming potential of service on airline reservation systems for AI-facilitated micro services and cloud computing and provide possible suggestions for overcoming the current challenges to harness great efficiency from them.

关键词： Cloud computing Reviews Scalability System performance Microservice architectures Pricing Computer architecture Artificial intelligence Predictive analytics Airline industry

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2nd EAI international conference on Edge computing and IoT: systems, Management and Security, ICECI 2021

2nd EAI International Conference on Edge Computing and IoT: ...

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2nd EAI international conference on Edge computing and IoT: systems, Management and Security, ICECI 2021

ISBN: (纸本)9783031042300

The proceedings contain 12 papers presendted at a virtual meeting. The special focus in this conference is on Edge computing and IoT: systems, Management and Security. The topics include: intelligent Vocal Training Assistant System;wearable-Based Human Emotion Inference System;committee Selection Based on Game Theory in Sharding Blockchain;green Transition driven Carbon Trading Mechanism and Design Based on Urban Vehicle Trajectory data;matching with Externalities for Device Assignment in IoT Sharding Blockchain;openflow-Extended Queue Scheduling for Multi-tenants in Access Network;Research and Improvement on Detection Algorithm of SCMA System;a Survey of Adversarial Attacks on Wireless Communications;adversarial Examples in Wireless Networks: A Comprehensive Survey;headmotion: Human-Machine Interaction Requires Only Your Head Movement.

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Energy Management, Design Constraints and Solution for data Dissemination in Wireless Sensor Networks 3

Energy Management, Design Constraints and Solution for Data ...

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3rd international conference on intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Agbulu, G. Pius Kumar, G. Joselin Retna Gunasekar, S. Srm Institute of Science and Technology Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai603203 India Srm Institute of Science and Technology Tamil Nadu Chennai603203 India Sree Vidyanikethan Engineering College AndhraPradesh Sree Sainath Nagar Tirupathi 517102 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Wireless sensor network (WSN) technology has become an essential constituent of our lives in health care, homes, food, and vehicles. Nonetheless, the sensors in WSNs naturally have limited device lifecycle due to serious energy-constraints, and so cannot guarantee to satisfy the QoS (Quality of Service) demands of different monitoring applications. This presents a significant setback to the effective application of WSNs in real-word. Therefore, to enhance the lifecycles of WSNs, it is fundamental to improve the energy management at the nodes as they are mostly set-out in geographically isolated zones. To achieve this, several communications mechanisms for energy-management and reliable routing operations is required. In this paper, we examine energy-management and the reliable routing mechanisms of WSNs with specific emphases on mobile and static deployments. We also consider fundamental design challenges and mechanisms to deal with routing challenges posed by innate constraints of the node. We assess various recent routing schemes for energy management in WSNs and propose an application-specific time-driven distributed routing scheme named Wi-Fi-based multi-hop routing algorithm (WMHR). We show the performance of the proposed WMHR in terms of energy -saving in comparison with other classical time-driven routing models. © 2022 IEEE.

关键词： Energy management

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Predictive Risk and Complexity Score Assessment Model for Cloud computing

Predictive Risk and Complexity Score Assessment Model for Cl...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Kavitha Ayappan J.M Mathana J Thangakumar Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

With the rapid growth in information technology and being called the Digital Era, it is very evident that no one can survive without internet or ICT advancements. The day-to-day life operations and activities are dependent on these technologies. The latest technology trends in the market and industry are computing power, Smart devices, artificial intelligence, Robotic process automation, metaverse, IOT (Internet of things), cloud computing, Edge computing, Block chain and much more in the coming years. When looking at all these aspect and advancements, one common thing is cloud computing and data which must be protected and safeguarded which brings in the need for cyber/cloud security. Hence cloud security challenges have become an omnipresent concern for organizations or industries of any size where it has gone from a small incident to threat landscape. When it comes to data and cyber/ cloud security there are lots of challenges seen to safeguard these data. Towards that it is necessary that everyone must be aware of the latest technological advancements, evolving cyber threats, data as a valuable asset, Human Factor, Regulatory compliance, Cyber resilience. To handle all these challenges, security and risk prediction framework is proposed in this paper. This framework PRCSAM (Predictive Risk and Complexity Score Assessment Model) will consider factors like impact and likelihood of the main risks, threats and attacks that is foreseen in cloud security and the recommendation of the Risk management framework with automatic risk assessment and scoring option catering to Information security and privacy risks. This framework will help management and organizations in making informed decisions on the cyber security strategy as this is a data driven, dynamic & proactive approach to cyber security and its complexity calculation. This paper also discusses on the prediction techniques using Generative AI techniques.

关键词： Industries Cloud computing security Computational modeling Organizations Predictive models data models Complexity theory

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